ALBERT

Beschreibung: Ulansuhai, the largest shallow lake of the Yellow River of China, is an important component of the Hetao region irrigation system. Many concerns have concentrated on its water quality, which affects the local water security and sustainable economic development. In this study, the water pollution index (WPI), an effective water quality evaluation method, was used to compare the pollution levels among pollution indicators and to determine the major pollution indicators. The regime shift index (RSI) approach was employed to identify the water quality trends. Cluster analysis and Daniel trend test methods were employed to analyse the inner-annual and inter-annual spatio-temporal trends of the typical water quality indicators (e.g., total nitrogen (TN), total phosphorus (TP), dissolved oxygen (DO), and chemical oxygen demand (COD)) in Lake Ulansuhai. The results show that the water quality of Ulansuhai improved from 1998 to 2017; spatial variations in the WPITN, WPITP, and WPIDO followed the order of inlet 〉 centre and outlet, while spatial variations in the WPICOD showed the order of outlet 〉 inlet 〉 centre. TN was the critical pollution indicator throughout the year. In 2017, the dry season and wet season were determined using cluster analysis. The WPICOD was higher than the WPITN, WPITP, and WPIDO in the dry season, while the WPITN, WPITP, and WPIDO were higher than the WPICOD in the wet season. WPI was grouped into three clusters: highly polluted regions, moderately polluted regions, and less polluted regions, However, there is a discrepancy between the three polluted regions that were divided into the dry season and the wet season. The WPICOD was highest among all pollution indicators in 2017. Major sources of pollution that contribute to the deterioration of water quality include inner-annual or inter-annual pollution, agricultural non-point pollution, point source pollution, and internal pollution. This study provides useful information for authorities to effectively manage water quality and control water pollution.

Beschreibung: Mathematics, Vol. 6, Pages 61: A Developed Artificial Bee Colony Algorithm Based on Cloud Model Mathematics doi: 10.3390/math6040061 Authors: Ye Jin Yuehong Sun Hongjiao Ma The Artificial Bee Colony (ABC) algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

Beschreibung: The elderly are susceptible to depression, especially empty nesters. This study investigated the risk factors associated with depression in empty nesters. The participants were selected via multi-stage random cluster sampling. Depression symptoms were assessed using the Geriatric Depression Scale. The questionnaire surveyed demographic characteristics, underlying diseases, behaviors and lifestyles, negative life experiences, social support, self-care ability, etc. Chronic health conditions, such as being overweight/obese, smoking cessation, cancer, and chronic pain, as well as negative life experiences, such as the death of a loved one and financial loss, increased the risk of depression in empty nesters. In addition, the incapability of performing self-care also increased the risk of depression in empty nesters. The prevalence of depression in empty nesters was high. Being overweight/obese, cancer, chronic pain, smoking cessation, the death of a loved one, financial loss, and the incapability to deliver self-care were risk factors for depression in empty nesters. It is recommended that comprehensive measures be taken to enhance health interventions for this population, including encouraging a proper diet and physical activity for weight control, providing psychological counseling after negative life experiences, and teaching correct methods of smoking cessation.

Beschreibung: The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average.

Beschreibung: In recent years, technological mergers and acquisitions (M&As) have become important strategic tools for enterprises to access and utilize new external knowledge. In particular, in the biopharmaceutical industry, M&A activities are actively being progressed due to an increase in new drug development costs, a decrease in R&D productivity, and the patent expiration of blockbuster drugs. However, there is a lack of research on the integrated view of (1) the acquirer’s capability and (2) the dyadic relationship of the knowledge base between the acquirer and target on the innovation performance of the acquirer. Furthermore, there are few empirical studies on the impact of these factors on ambidextrous innovation; that is, exploitative and explorative innovation. Therefore, with this integrated view in mind, this study analyzed the effect of each factor on the exploitative and exploratory innovation performance of the acquirer. A negative binomial regression was conducted using patent data to measure the innovation outcome of the acquirer after M&A. The findings suggest that (1) the acquisition experience of the acquirer and (2) the technological commonness between the acquirer and the target both had a significant impact on the exploitation and exploration innovation performance.